Initial examination of a multidimensional model of trauma-related guilt: Applications to combat veterans and battered women

Guilt is conceptualized as a multidimensional construct consisting of negative affect and a set of interrelated cognitions. Guilt magnitude is thought to be a function of the magnitudes of six variables posited as primary components of guilt: a negative event, distress, perceptions of responsibility, lack of justification, wrongdoing, and false beliefs about preoutcome knowledge. The model was tested with samples of Vietnam veterans and battered women. Participants rated their reactions to and perceived roles in trauma-related events. Among Vietnam veterans, distress ratings were highly correlated with guilt severity. Cognitive guilt-component variables were significantly correlated with guilt in both groups. In multiple regression, guilt components accounted for 61% of variance in veterans' guilt and 44% of variance in women's guilt. Among veterans, distress ratings were highly correlated with measures of PTSD and depression. In both groups, cognitive guilt-component variables were positively correlated with psychopathology. Results support the view that beliefs about one's role in trauma are important factors in posttrauma adjustment.     

A Self-report Family Data Form for family therapy

Family therapies which aim to begin treatment in the first session leave the practitioner at a disadvantage regarding the collection of demographic, historical, and genealogical data that may be useful as treatment proceeds. Not wanting to disrupt interactional reciprocity and flow, the therapist cannot perform methodical anamnesis. The Self-report Family Data Form is presented for collecting useful biographical data from families in treatment without interfering with the clinical interaction.     

REWARD SIZE AND GAME PLAYING BEHAVIOR

S haw J. Reward size and game playing behavior. Scand, J. Psychol ., 1972, 13 , 121–132.—The results of studies dealing with the effects of heightened incentives in game playing situations present a complicated pattern of findings ranging from those favoring cooperation, through those indicating no relation, to those favoring competition. A critical review of these studies is made followed by attempts to reconcile the divergent findings. These include: ( a ) Gallo's (1968 b ) index of average payoff per trial, and ( b ) Oskamp & Kleinke's (1970) notion that large rewards may induce individualistic behavior. Suggestions are made for further research on reward size as related to ( a ) noneconomic rewards, ( b ) matrix structure, ( c ) reward pattern, ( d ) multichoice methodologies, and ( e ) parametric values.     

Computing the von Neumann-Morgenstern characteristic functionυ(S) for cooperativen-person transferable utility normal form games: LP and saddlepoint solutions

An APL computer program is described for computing the von Neumann-Morgensterncharacteristic function of cooperativen-person transferable utility games in normal form. The program is designed to stand alone, or it can be used in conjunction with other programs for game theoretic computation that will be introduced in future articles.     

A content analysis of sexual themes in Playboy cartoons

Critics of pornography have argued that the way men and women are portrayed in these media create images that can result in harmful gender socialization. The cartoons from all 1985 issues of Playboy were coded for the presence or absence of four themes that have been identified by these critics. It was found that, when these themes appeared, women were more often the victim of sexual coercion, were more often depicted as sexually naive and childlike, and were more often shown with a more attractive body than were the men in the cartoons.The authors wish to thank Mike Chambers, Bruce Healy, Cathy Thompson and Micheala Wilczynski for their help with the project.     

APL functions for interactive data analysis: Principal components analysis

APL functions to support principal components analysis are presented: a general-purpose function to obtain eigen values and eigen vectors, a more specialized function to convert these into the results commonly given by principal components analysis, and a user interface function that accesses filed data, offers flexibility in data selection, and produces labeled output. A brief introduction to the logic and uses of principal components analysis is included. General-purpose support functions to simplify file use and to increase the range of options available to the user are also provided.